Method and system for creating events and matching users via blended profiles

ABSTRACT

Certain embodiments teach a method and system for pairing users, whom do not know each other for an event, on a social networking platform. The social dining system matches people with common interests and organizes an offline event, such as a meal at a restaurant, based on user profiles, availability, and other attributes. For example, the social dining system can schedule a group meal for a user, and five to seven other people that share an interest with the user. In cases where a user prefers to be attend events with at least one known user, a single profile for the user and the known user is created as a mutual or a blended profile. The system then creates an event by matching the mutual profile with other members using a blending algorithm.

CROSS REFERENCES

This patent application claims the benefit of U.S. ProvisionalApplication No. 61/488,470, entitled “METHOD AND SYSTEM FOR CREATINGEVENTS AND MATCHING USERS VIA BLENDED PROFILES,” filed May 20, 2011, andis hereby incorporated by reference.

BACKGROUND

People routinely rely on social networking and social media websites onthe Internet to renew former ties, maintain current relationships, or tocreate new connections. Current websites facilitate online interactionsbetween users having a common activity, interest, profession, cause,etc. These systems allow a user to unilaterally communicate with his/hersocial network by posting status updates or broadcasting information.Present-day social networking platforms additionally offer users theability to join or create an interest or activity group within thewebsite. There are also online dating services which essentially pairone user with another user.

While the most pervasive way for individuals to connect online isthrough these social networking and dating websites, this method hasseveral shortcomings and inefficiencies. For example, current networkingplatforms primarily focus on interaction in the virtual world (i.e., onan online basis) and this exchange is typically amongst prioracquaintances. Some primarily operate as a bulletin board or forum inwhich a meeting with fellow members must be initiated by an organizingmember or leader. As such, the development of a personal interaction inan offline setting relies on the enthusiasm, focus, resources, and timeconstraints of the individuals. In the instance of online datingwebsites, the service primarily fosters interaction between users on aone-to-one basis, in which the risks of rejection, deception, and evenphysical danger exist in some cases.

As such, in a world where people are relying on virtual interactions toconnect with others, it is desirable to implement a system thatincreases human (i.e., offline) connections, and provides an opportunityfor real people to meet in the real world. A system is needed thatharnesses the utility of the Internet to arrange an event that matches auser, and at least one other individual known to the user, with otherpeople in a social setting.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of a system and method for creating events and matching usersvia mutual profiles are illustrated in the figures. The examples andfigures are illustrative rather than limiting.

FIG. 1 depicts an example environment in which the method and system formatching users and creating events via a mutual profile (also known as a“blended profile”) can be implemented, according to one embodiment.

FIG. 2A depicts an example block diagram illustrating a social diningsystem which arranges an event by matching a mutual/blended profile withother entities (e.g., individual members, blended profiles) using ablending algorithm, according to one embodiment.

FIGS. 2B-2E depict a block diagram illustrating example datasets.

FIG. 3 depicts a flow diagram illustrating an example process ofarranging an event by matching a mutual/blended profile with otherentities (e.g., individual members, blended proles) using a blendingalgorithm, according to one embodiment.

FIGS. 4A-4E depict example screenshots displayed on an electronicdevice, according to one embodiment.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known or conventional details are not described in orderto avoid obscuring the description.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Reference inthis specification to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thedisclosure. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Moreover, various features are describedwhich may be exhibited by some embodiments and not by others. Similarly,various requirements are described which may be requirements for someembodiments but not other embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. The use of examplesanywhere in this specification including examples of any terms discussedherein is illustrative only, and is not intended to further limit thescope and meaning of the disclosure or of any exemplified term.Likewise, the disclosure is not limited to various embodiments given inthis specification.

The terminology used in the description presented below is intended tobe interpreted in its broadest reasonable manner, even though it isbeing used in conjunction with a detailed description of certainspecific examples of the invention. Certain terms may even be emphasizedbelow; however, any terminology intended to be interpreted in anyrestricted manner will be overtly and specifically defined as such inthis Detailed Description section.

Certain embodiments teach a method and system for pairing users, whom donot know each other for an event, on a social networking platform. Thesocial dining system matches people with common interests and organizesan offline event, such as a meal at a restaurant, based on userprofiles, availability, and other attributes. For example, the socialdining system can schedule a group meal for a user, and five to sevenother people that share an interest with the user. In cases where a userprefers to be attend events with at least one known user, a singleprofile for the user and the known user is created as a mutual or ablended profile. The system then creates an event by matching the mutualprofile with other members using a blending algorithm (discussed indetail further herein).

By matching online users for an event that takes place outside thevirtual world, the method and system facilitates offline interaction inthe real world. Small intimate groups allow a user to connect with andmake new friends, meet potential business partners, and potentially finda significant other. Moreover, the social dining system facilitates theability of individuals traveling for business or leisure to meet newpeople in a destination city.

The social dining system enables users to create a plurality of profilessuch as a personal profile, a professional profile, a food profile, amutual profile, and so on. Based on the indicated interests (or“attributes”) in these profiles, events can be created based, at leastin part, on a commonality or similarity between users. With a mutualprofile, the system permits a user to invite an existing friend to joinin on the social dining adventure and thus, attend an event withouthaving to go it alone,

The mutual profile's advantage is derived from the general observationthat most people prefer meeting new people in a group setting. First,meeting people with at least one other person reduces the risks ofrejection, deception, and incompatibility. Also, jointly interactingwith unfamiliar people is more comfortable and increases the ease ofdetermining common interests among the group. Further, social norms allbut ensure that a user will not engage in deception when there is afamiliar party involved who can confirm or discredit any assertions bythe user. The system enables an individual to leverage his or her socialnetwork of friends and acquaintances to meet new people in a groupsetting.

In one embodiment of a mutual profile, the system facilitates theability of a user to attend events with an acquaintance, friend,relative, colleague, or significant other. The system allows at leasttwo individuals (also known as “multiples”) in a network to connect withother multiples based on a common interest in the mutual profile. Thematching of multiples based on a common interest in a particular subjectis more likely to lead to more interesting conversations and lastingengagements. The system also provides visibility to businesses such asrestaurants, artisan food suppliers, winemakers, etc. whereby users inthe system can easily make referrals. As used herein, the term“restaurants” broadly refers to any entity that has goods and/orservices to offer and desires to display a listing to users on theirelectronic devices 110A-N. Restaurant is intended to refer to any entitythat can provide an offline activity, product, and/or service to a userand does not merely refer to an entity that offers food products andservices.

FIG. 1 depicts an example block diagram 100 of a system including aplurality of electronic devices 110A-N, an online payment processingservice 170, a central server 120, and a profiles dataset 130, arestaurant dataset 132, a friends dataset 134, and an events dataset 136coupled via a network 150, according to one embodiment.

The plurality of electronic devices 110A-N can be any system and/ordevice, and/or any combination of devices/systems, for presentinginformation to a user and potentially establishing a connection via thenetwork to the central server 120. Examples of electronic devices 110A-Ninclude, but are not limited to, personal computers, laptop computers,computer dusters, mobile telephones, mobile notebooks/tablets, personaldigital assistants, and the like. The electronic devices 110A-N may becoupled to the network 150 via a wired or wireless connection or by anyother method.

Users of the electronic devices 110A-N have the ability to access thesocial dining system by initiating, downloading, or installing softwareor an application. In one embodiment, the user initiates a web browserto peruse electronic content and to establish online communications. Inanother embodiment, the user interfaces with an executable program or astand-alone application to access the social dining system. For example,a user may access a mobile application on his or her cell phone toaccess features of the system. The social dining system (once initiated,installed, downloaded, etc.), may present items to the user such asdata, images, search tools, hyperlinks, community and viral features,and more. In some implementations, this access may be granted after aregistration process in which the user provides a valid email addressand a password. In an alternative or further implementation, this accessmay be granted after the user agrees to purchase a membership or servicefor a predetermined period such as a subscription.

The network 150 may include, but is not limited to, a telephonic networkand an open network, such as the Internet. The network 150 may be anycollection of distinct networks operating wholly or partially inconjunction to provide connectivity to the electronic devices and mayappear as one or more networks to the serviced systems and devices. Inone embodiment, communications over the network 150 may be achieved by asecure communications protocol, such as secure sockets layer (SSL), ortransport layer security (TLS).

In addition, communications can be achieved via one or more wirelessnetworks, such as, but is not limited to, one or more of a Local AreaNetwork (LAN), Wireless Local Area Network (WLAN), a Personal areanetwork (PAN), a Campus area network (CAN), a Metropolitan area network(MAN), a Wide area network (WAN), a Wireless wide area network (WWAN),Global System for Mobile Communications (GSM), Personal CommunicationsService (PCS), Digital Advanced Mobile Phone Service (D-Amps),Bluetooth, Wi-Fi, Fixed Wireless Data, 2G, 2.5G, 3G networks, enhanceddata rates for GSM evolution (EDGE), General packet radio service(GPRS), enhanced GPRS, messaging protocols such as, TCP/IP, SMS, MMS,extensible messaging and presence protocol (XMPP), real time messagingprotocol (RTMP), instant messaging and presence protocol (IMPP), instantmessaging, USSD, IRC, or any other wireless data networks or messagingprotocols.

The datasets 130-136 may store information such as software, descriptivedata, images, video, system information, and/or any other data itemutilized by modules of the central server 120 for operation. Thedatasets 130, 132, 134, and 136 may be managed by (but not limited to) adatabase management system (DBMS), Oracle DB2, Microsoft Access,Microsoft SQL Server, PostgreSQL, MySQL, FileMaker, etc.

The datasets 130, 132, 134, and 136 can be implemented viaobject-oriented technology and/or via text files, and can be managed bya distributed database management system, an object-oriented databasemanagement system (OODBMS) (e.g., ConceptBase, FastDB Main MemoryDatabase Management System, JDOInstruments, ObjectDB, etc.), anobject-relational database management system (ORDBMS) (e.g., Informix,OpenLink Virtuoso, VMDS, etc.), a file system, and/or any otherconvenient or known database management package. An example set of datato be stored, managed, or presented, in the profiles dataset 130,restaurant dataset 132, friends dataset 134, and events dataset 136 isfurther illustrated in FIGS. 2B-2E.

The central server 120 is, in some embodiments, able to communicate withelectronic devices 110A-N via the network 150. Additionally, the centralserver 120 is able to directly communication with restaurants forpurposes of reservations, advertising, promotions, demographic analysis,and the like. In some embodiments, the central server 120 is able tomonitor the consumer's use of an electronic device to determine whichrestaurants are of interest to the user, thus allowing for morecustomization and intelligence in matching, as related to mutualprofiles, events, targeted advertising, promotions and the like. Thecentral server 120 can also store and retrieve data from the datasets130, 132, 134, and 136.

FIG. 2A depicts an example block diagram 200 illustrating a socialdining system for matching users and creating events via a mutualprofile. The system includes a central server 230 coupled to a profilesdataset 202, a restaurants dataset 204, a friends dataset 206, and anevents dataset 208.

Referring back to FIG. 2A, the central server 230 includes a networkinterface 232, firewall (not shown), communications module 234, a userinterface module 236, a restaurant module 242, a friends module 244, amutual profile module, and an event scheduling module 246. Additional orfewer modules may be included. The central server 230 may becommunicatively coupled to the profiles dataset 202, the restaurantsdataset 204, the friends dataset 206, and the events dataset 208 asillustrated in FIG. 2A. In some embodiments, the profiles dataset 202,the restaurants dataset 204, the friends dataset 206, and the eventsdataset 208 are partially or wholly internal to the central server 230.

In the example of FIG. 2A, the network interface 232 can be one or morenetworking devices that enable the central server 230 to mediate data ina network with an entity that is external to the central server, throughany known and/or convenient communications protocol supported by thecentral server and the external entity. The network interface 232 caninclude one or more of a network adaptor card, wireless networkinterface card, router, access point, wireless router, switch,multilayer switch, protocol converter, gateway, bridge, bridge router,hub, digital media receiver, and/or repeater.

A firewall can, in some embodiments, be included to govern and/or managepermission to access/proxy data in a computer network, and track varyinglevels of trust between different machines and/or applications. Thefirewall can be any number of modules having any combination of hardwareand/or software components able to enforce a predetermined set of accessrights between a particular set of machines and applications, machinesand machines, and/or applications and applications, for example, toregulate the flow of traffic and resource sharing between these varyingentities. The firewall may additionally manage and/or have access to anaccess control list which details permissions including for example, theaccess and operation rights of an object by an individual, a machine,and/or an application, and the circumstances under which the permissionrights stand. In some embodiments, the functionalities of the networkinterface 232 and the firewall are partially or wholly combined and thefunctions of which can be implemented in any combination of softwareand/or hardware, in part or in whole.

In the example of FIG. 2A, the central server 230 includes thecommunications module 234 or a combination of communications modulescommunicatively coupled to the network interface 232 to manage aone-way, two-way, and/or multi-way communication sessions over aplurality of communications protocols. In one embodiment, thecommunications module 234 receives data (e.g., video data, textual data,video files, etc.), information, commands, requests (e.g., text-based),and/or text-based messages over a network.

Since the communications module 234 is typically compatible withreceiving and/or interpreting data originating from variouscommunication protocols, the communications module 234 is able toestablish parallel and/or serial communication sessions with users ofremote client devices for data and command exchange (e.g., userinformation and/or restaurant listings). In addition, the communicationsmodule 234 can manage log-on requests received from one or morethird-parties such as users or restaurants connecting to the centralserver 230 to submit profile or other related information.

For example, the platform may utilize a username/email and passwordidentification method for authorizing access. The communications module234 can gather data to determine if the user is authorized to access thesystem and if so, securely logs the user into the system. In otherembodiments, other forms of identity authentication, including but notlimited to, security cards and digital certificates can be utilized andare contemplated, in accordance with this disclosure. A user may be ableto specify and/or obtain a logon ID after subscribing or registering.

In the example of FIG. 2A, the central server 230 is coupled to aprofiles dataset 202, a restaurants dataset 204, a friends dataset 206,and an events dataset 208. The central server 230 can be implementedusing one or more processing units, such as server computers, UNIXworkstations, personal computers, and/or other types of computes andprocessing devices. In the example of FIG. 2A, the central server 230includes multiple components coupled to one another and each componentis illustrated as being individual and distinct. However, in someembodiments, some or all of the components, and/or the functionsrepresented by each of the components can be combined in any convenientand/or known manner. For example, the components of the central servermay be implemented on a single computer, multiple computers, and/or in adistributed fashion.

Thus, the components of the central server 230 are functional units thatmay be divided over multiple computers and/or processing units.Furthermore, the functions represented by the devices can be implementedindividually or in any combination thereof, in hardware, software, or acombination of hardware and software. Different and additional hardwaremodules and/or software agents may be included in the central server 230without deviating from the spirit of the disclosure.

One embodiment of the central server 230 includes a user interfacemodule 236. The user interface module 236 may be any combination ofsoftware agents and/or hardware components able to interact andcommunicate with one or more users 160A-M. The user interface module 236allows users to manipulate the social dining system and allows thesocial dining system to indicate the effects of the users' manipulation.The user interface module 236 is, in most instances, able to presentinformation to the user and query users' information, such as user logininformation, profile data, and availability, etc. and receiveinformation in response. In a further embodiment, the user interfacemodule 236 handles communications with members and non-membersincluding, but not limited to, alerts, invitations, pending requests,event notices, and the like.

One embodiment of the central server 230 includes a restaurant module238. The restaurant module 238 may be any combination of software agentsand/or hardware components which manages, organizes and sortsinformation related to restaurants. In one embodiment, the restaurantmodule 238 enables users to search, browse, and generally interface withthe information contained in the restaurants dataset 204 as well asindicate their preferences for restaurant(s). Users may search for arestaurant by name or search for a restaurant near/within a specificcity, state, or zip code. The information displayed to a user includesrestaurant name, description, whether alcohol is served, type ofcuisine, location, directions, phone number, restaurant ratings (e.g.,Michelin, Zagat, user), promotions, an icon or image of the restaurant,a hyperlink to the restaurant's web page or to a mechanism through whichreservations can be made, and similar features. A featured restaurantmay be presented to the user in an area of the user interface that ishighly visible to users. In an alternative or further embodiment, therestaurant module 238 allows users to filter a pre-existing list ofrestaurants by certain restaurant attributes, such as distance, price(e.g., under $20, $21-30, $31-40, above $40) or cuisine. The restaurantmodule 238 also allows users of the social dining system to submit arating and/or review of a particular restaurant.

In some embodiments, the restaurants module 238 includes a monitoringmodule (not shown) or some intelligent mechanism through which a user'sselections may be tracked. One method for determining whether a user isactively interested in a particular restaurant is by tracking the user'smouse-overs, mouse clicks, interaction, and hyperlink selections. Othermethods of tracking user activity may also be implemented. For example,the consumer's keywords may be monitored to determine other restaurantsthat a user may like. In turn, the restaurant module 238 may presentadditional functionalities such as a customized view of top-rated,recommended, or suggested restaurants.

In some embodiments, the restaurant module 238 includes a restaurantinterface module (not shown) through which one or more restaurants caninteract with the social dining system. The restaurant interface modulecan handle functions and features including, but not limited to, diningreservations, advertising, promotions, compensation, member conversion,and the like. For instance, a restaurant may provide promotions andcoupons in coordination with the social dining platform wherein thesuccess of the campaign (e.g., rate at which the restaurant converts anindividual to a social dining user, rate at which the promotion attractssocial dining users) may be assessed.

In one embodiment, the restaurant module. 238 allows a user to addrestaurants that the user would like to eat at to a list of restaurants,termed a “restaurant wish list.” Further, users can gauge the interestand popularity of a restaurant through an indication that shows thenumber of other users that have added the particular restaurant to theirrestaurant wish list. The restaurant module 238 allows a user to managethe restaurants in his or her restaurant wish list including rankordering, removing, adding, modifying, rating restaurants and the like.In turn, users' assessed preference of a restaurant via e.g., rankordering or rating can be accounted for as an attribute in the blendingalgorithm.

One embodiment of the central server 230 includes a friends module 240.The module 240 may be any combination of software agents and/or hardwarecomponents which manages, organizes, and sort information related to auser's association with other individuals, including individuals who areand are not members of the social dining system. In one embodiment, thefriends module 240 defines certain associations or affinities betweenindividuals such as business associate, friend, significant other,spouse, and the like and allows a user to manage his or her connectionand activities with those individuals. Further, the friends module 240allows users to import contacts and friends from an external sourceincluding social networking websites such as Facebook™. For example, thefriends module 240 informs the user of current friends, recently-addedfriends, friend requests, restaurants recently rated by friends, and thelike. Moreover, the friends module 240 enables a user to add or invitenon-members to register and join the social dining system.

In the same or alternative embodiment, the friends module 240 allows auser to browse a directory of unknown (i.e., non-friend) members whomare part of the social dining system and/or whom the user would like tointeract with, and to add a member to a list of target members, termed a“member wish list.” To facilitate a user's perusal of fellow members,the directory of members may be organized by attribute, such as byinterests (e.g., coffee and conversation), activities (e.g.,basketball), location, willingness to try new foods, cuisine, restaurantwish list, any profile attribute, and the like. Further, users can gaugethe appeal and popularity of a member through an indication that showsthe number of other members that have added the particular user to theirmember wish list. In one embodiment, the friends module 240 protects theprivacy of a member by displaying only select information to a user,such as the member's first name, location, and number of times themember has been wish-listed. The friend module 240 allows a user tomanage the members in his or her wish list, including removing, adding,rank-ordering members and the like. In turn, the user's assessedpreference for a member such as rank ordering can be accounted for as anattribute in the blending algorithm.

One embodiment of the central server includes a mutual profile module238. The mutual profile module 238 may be any combination of softwareagents and/or hardware components able to create, manage, recommend, orsuggest a mutual profile. A mutual profile (also called a “blendedprofile”) is a concept that allows two or more entities to aggregate andact as a single merged entity. For example, if a user prefers to attendevents with at least one individual that the user knows, a singleprofile comprised of the user and another member, can be created. Whilea blended profile is herein described as an amalgamation of a user andanother known member, the blended profile is not limited to users of thesocial dining system, but can include other entities such as pets, kids,historical or political figures, fictional characters, celebrities, andthe like. Subsequently, the system creates an event by matching theblended profile to other entities, using a blending algorithm. While thecreation of a blended profile is herein described in terms of twoentities, the blended profile is not limited in number, but can includetwo or more entities.

In some cases, a user creates a new blended profile with an individualwho is currently a member of the social dining system. In one instance,the user places a query or search by entering an identifier such asname, email, avatar, and the like. In some instances, the user mustfirst send a friendship request to the member before a blended profilecan be created. After the member accepts the user's friendship request,the user can then immediately create the blended profile. In otherinstances, the user must send a blended profile request to the friend.In either instance, the member must accept the user's request to be apart of the blended profile. As a registered user of the social diningsystem, the member may have one or more associated profiles (e.g.,personal, lifestyle, professional, foodie, etc.) in the system. As such,a brief description, or snapshot of the blended profile can be createdwhich highlights significant attributes of the individual members thatmake-up the blended profile.

In some cases, a user creates a blended profile with another individualthat is currently not a user of the social dining system. Acommunication such as an email or a text message may be sent to theindividual, stating that the user has invited the individual to connectvia the social dining system. In some embodiments, a temporary accountsuch as a guest user login, a trial account, or the like can be used bythe individual to connect with the user, whereby the individual is notrequired to register with the social dining system. In otherembodiments, the individual must register with social dining service inorder to connect with the user. After the individual registers with thesocial dining system, the individual becomes a member of the socialdining system. With both the user and the member being registered usersof the social dining system, either individual may resume the steps tocreate a blended profile. As described above, there may be an additionalrequirement in which the members must be friends in order to create theblended profile. After a blended profile consisting of the user and themember is created, the social dining system uses a blending algorithm toassist in arranging an event in which members of the blended profileinteract with other social dining members.

In some cases, the mutual profile module 238 prompts the user to createa blended profile with another member as a suggested blended profile.The social dining system can automatically parse the member-set andsuggest an individual with whom the user should create a blendedprofile. For example, if a user met and “clicked/hit-it-off” with anindividual at a past event (e.g., “group meal”) via user wish list orsimilar feedback mechanism, the social dining system may recommendreconnecting with that individual by creating a blended profile. In oneembodiment, this individual can be a current member of the social diningsystem or a friend of (i.e., known to) the user. In another embodiment,the individual may be an unknown member of the social dining system oranother individual, possibly not a member of the social dining system(e.g., celebrity, historical figure, etc.). In turn, the set ofindividuals from which the social dining system can recommend include,but is not limited to, a users existing set of friends on the socialdining system or on another social networking website, the completenetwork of users on the social dining system, and the like.

In one embodiment, the single merged entity, or blended profile, is anabstract data structure, or object, which brings together the componentsof a data source with the procedures that manipulate them. For example,the object can be a default constructor with no parameters andread/write properties, wherein the read/write properties for theencapsulated data form the boundary conditions of the blending algorithm(discussed in more detail further herein). Further, the merged entitycan be a row in a table, with each property representing the value of acolumn for that table.

In this embodiment, two or more instances of the same object type aremerged to form a single representative entity. This can be accomplishedby creating a vector space of all of the properties between the two ormore instances. Next, measurements of the vector space distance aredetermined and form a new abstract structure, wherein the propertyvalues for this new structure are the mathematical distancemeasurements. After the two or more instances are merged into a singlemerged entity or blended profile, the blended profile can be treated asone for further processing and analysis, such as cluster analysis,comparison analysis, and the like.

One embodiment of the central server includes an event scheduling module246. The event scheduling module 246 may be any combination of softwareagents and/or hardware components able to aggregate at least one blendedprofile with at least one other user for an event. In oneimplementation, the user indicates his or her ability to attend futureevents on a calendar (Le., available date(s) and time) and preferredlocation (e.g., address, zip code, city, country, state, region, and/orarea). In some cases, if the user does not enter his availability, theuser is not invited to an event. Moreover, the event scheduling module246 can inform the user of pending, scheduled, confirmed, and pastevents, and other similar status updates.

In one embodiment, the blending algorithm pairs a blended profile withat least one other user of the social dining system. In a further oralternative embodiment, the blending algorithm joins a blended profilewith one or more other blended profiles. The blending algorithm can takeinto account a number of attributes in the process of matching a blendedprofile with another entity [i.e., individual(s), blended profile(s)].For example, the blending algorithm can take into account mutualavailability, purpose (e.g., networking), activities, interests,lifestyle attributes, preferred dining locations, willingness to try newfoods, favorite foods, and/or other attributes. Moreover, the blendedprofile's mutual friend wish list, restaurant wish list, and/orrestaurant ratings can be taken into consideration in the pairing of ablended profile with another entity. Those skilled in the art willrecognize that the blending algorithm can join a blended profile withother entities based upon additional attributes beyond the describedelements above.

In one embodiment, the blending algorithm is implemented within amanaged Dynamic Link Library (DLL). For the purposes of thisdescription, the library and algorithm will be referred to collectivelyas the kernel. The overall design of the kernel will search a dataset oflike objects for matches. As a simple example, a match occurs if thecolumn-names of a data source entity match the column-names of the seedentity (i.e., the object to be matched). A list of all possible dataelements within a data source entity is provided to the algorithm. Insome cases, for each element within this list, the system assigns anumeric/ordered 10. Further, the blending algorithm creates vectorsbetween two like objects that form a match and treat the pair of objectsas if they were one target. In a further embodiment, the kernel isdesigned as a data-source filter, whereby dynamic queries can be createdbased on the blending algorithm.

In some embodiments, each property within the object can be weighted ona three-point weighting system. For example, the weighting can beapplied wherein a weight of two is applied if the item or property(e.g., attribute) is required and must be present in the matchedentities, a weight of one is applied if the property in the matchedentities is moderately important, and a weight of zero is applied if theproperty is not important and/or no matching is performed on this item.For example, if a seed entity contains an element with a weight of oneor two, then the element is used as a factor. If, for example, the seedentity has a weight of two for an element, this weighting forms apre-filter on possible matched elements.

In one embodiment, the blending algorithm performs Quality-Thresholding(QT) clustering, whereby entities are grouped into high-quality dusters.The blending algorithm determines among a set of entities, which has thegreatest similarity to a particular entity. In one embodiment, thealgorithm maintains high-quality dusters by ensuring the diameter oflarge dusters does not exceed a predetermined diameter threshold suchthat dissimilar entities become part of the same duster. If the vectorspace distance, or diameter between two entities does not exceed a givenuser-defined diameter threshold, then the two entities can be clusteredtogether. Next, other entities which minimize an increase in thediameter are added to the cluster. This occurs iteratively until noentity can be added to the duster without surpassing the given diameterthreshold.

In one embodiment, the blending algorithm randomly identifies an entity(e.g., blended profile) from a set of entities (e.g., blended profiles).In a further embodiment, the attributes of the selected entity (e.g.,blended profile) are also determined. Given the set of entities whereany entity in that set can match, the blending algorithm then identifiesa candidate entity with the greatest similarity to the selected entity.For example, if Entity A has a property “Foo” and Entity B also has aproperty “Foo”, the matching algorithm can cluster these entities. Ifthe total diameter of the blended entity combined with the candidateentity do not exceed a predetermined diameter threshold, then these twoentities are clustered together to form a first candidate cluster. Next,other entities that minimize the increase in cluster diameter areiteratively added to this cluster until no entity can be added to thisfirst cluster without surpassing the diameter threshold.

Next, the blending algorithm then identifies a second entity (e.g.,blended profile) from the set of entities (e.g., blended profiles). Theblending algorithm identifies a candidate entity with the greatestsimilarity to the second blended entity. Here, all entities in the setof blended entities are available for consideration for the secondcandidate cluster. Other entities that minimize the increase in clusterdiameter are iteratively added to the second cluster until no entity canbe added to this second cluster without surpassing the diameterthreshold.

Next, the algorithm iterates through all entities in the set of entitiesand forms a candidate cluster relative to each entity. In turn, thenumber of candidate clusters formed is the same as the number ofentities in the set of entities. Once a candidate cluster is formed foreach entity, all candidate clusters below the user-specified minimumsize are removed from consideration. As a result, the largest clusterthat remains (above the user-specified minimum size) is selected andretained as a QT cluster. The entities within this QT cluster are nowremoved from consideration and the remaining entities, outside of the QTcluster, will be used for the next round of QT cluster formation.

In this embodiment, the process repeats until the largest remainingcandidate cluster has fewer than the user-specified number of entities.The result is a set of non-overlapping QT clusters that meet a certainquality threshold for both size (with respect to number of entities) andsimilarity (with respect to maximum allowable diameter). Entities thatdo not form in any clusters will be grouped under an “unclassified”group. Lastly, a list of matched entities is returned for data sourceretrieval.

In some embodiments, the metric used to determine the distance betweenclusters include the farthest neighbor method, or a Complete Linkageanalysis, which calculates distance between clusters in hierarchicalcluster analysis. In a Complate Linkage analysis, the linkage functionspecifies the distance between two clusters as the maximalobject-to-object distance D(x_(i), y_(j)), where objects x_(i) belong tothe first cluster, and object y_(j) belong to the second cluster. Inother words, the distance between two clusters is computed as thedistance between the two farthest objects in the two clusters.Mathematically, the linkage function, or the distance between clusters Xand Y is described by the following expression: D(X,Y)=max d(x,y), wherex ∈ X and y ∈ Y, and d(x,y) is the distance between objects x and y andX and Y are two sets of objects (clusters).

FIGS. 2B-2D depict a block diagram illustrating examples of thesedatasets. FIG. 2B depicts a block diagram illustrating an exampleprofiles dataset 202 that stores personal profile data 202A,professional profile data 202B, mutual profile data 202C, and foodieprofile data 202D, according to one embodiment.

In the example of FIG. 2B, personal profile data 202A is stored indataset 202. Personal profile data includes information related to theuser's personal characteristics, demographics, and preferences. Personalprofile data includes, but is not limited to, information related tolifestyle such as a user's gender, date of birth, age (e.g., 18-24,25-31, 32-38, 39-45, 46-52, 53-59, 60-66, 67+), location, education(e.g., some high school, high school grad, some college, college grad,post-graduate, I'll tell you later), relationship status (e.g.,single-never married, in a relationship, married, divorced, separated,widowed, I'll tell you later), sexuality (e.g., straight, lesbian/gay,bisexual, I'll tell you later, rather not say), religion (e.g., atheist,Buddhist—Taoist, Christian, Christian—Catholic, Christian—LDS,Christian—Other, Christian—Protestant, Hindu, I'll tell you later,Islam, Jewish, None—agnostic, not religious, other, Scientology,Spiritual but not religious), political views (very conservative,conservative, middle of the road, liberal, very liberal, not political,tell you later, any). Personal profile data may also include informationon the user's diet (e.g., vegan, vegetarian, low-carb, gluten-free,unrestricted), objectives (i.e., “looking to”) (e.g., make new friends,network), interests (e.g., alumni connections, camping, coffee andconversation, business networking, cooking, dining out, fishing/hunting,gardening landscaping, hobbies and crafts, movies videos, museums andart, music and concerts, exploring new areas, nightclubs/dancing,performing arts, playing cards, playing sports, reading, shoppingantiques, travel/sightseeing, video games, volunteering, watchingsports, watching television, wine tasting), and the activities or sports(e.g., aerobics, auto racing/motocross, baseball, basketball,billiards/pool, bowling, cycling, football, golf, dancing, inlineskating/roller skating, martial arts, running, skiing, soccer, swimming,tennis/racquet sports, walking/hiking, weights/machines, yoga, othertypes of exercise, hockey, volleyball) in which the user participates.Personal profile data also includes information related to the personalcharacteristics of whom a user would like to meet. In some embodiments,this individual would be another user of the social dining system and ishereinafter termed, “member.” As such, personal profile data includesinformation related to the members sought-out by the user, termed“target members” and can include information related to the lifestyle,diet, interests, and activities/sports in which the target memberparticipates.

In the example of FIG. 2B, professional profile data 202B is stored indataset 202. Professional profile data includes information related tothe user's professional characteristics, demographics, and preferences.Professional profile data includes, but is not limited to, informationrelated to a user's employment status (e.g., full-time, part-time,homemaker, retired, self-employed, student, taking time off, work athome, I'll tell you later), annual income (e.g., less than $24,999,$25,000-$34,999, $35,000-$49,999, $50,000-$74,999, $75,000-99,999,$100,000-149,999, more than $150,000, I'll tell you later), careerlevel/title (e.g., internship, entry level, associate, mid-senior level,director, executive, I'll tell you later), industry (e.g.,advertising/marketing, artistic/musical/writer, banking/financialservices/real estate, clerical/administrative,computer-related/hardware, construction/craftsman, education/academicresearch, entertainment/media, executive/management, food service,hospitality/travel, internet/eCommerce, legal services,manufacturing/distributions, medical/health services,politics/government military, sales/business development,technical/science/engineering, transportation), and the like.Professional profile data also includes information related to theprofessional characteristics of whom a user would like to meet. As such,professional profile data can include information related to theemployment status, annual income, career level/title, industry, etc. ofthe target member. In some embodiments, the target member is aregistered user of the social dining system.

In the example of FIG. 2B, foodie profile data 202C is stored in dataset202. Foodie profile data includes information related to the user'seating habits, cuisine preferences, and related information. Foodieprofile data includes, but is not limited to information related to auser's favorite cuisines [e.g., Afghan, African, All Cuisines, American(New), American (Traditional), Argentinean, Armenian, Asian, Austrian,Bakeries, Barbecue, Belgian, Brazilian, Burmese, Cajun & Creole,Californian, Caribbean, Central European, Chilean, Chinese, Coffee Shops& Diners, Colombian, Cuban, Deli, Desserts, Dim Sum, Easter European,Eclectic & International, Egyptian, English, Ethiopian, Family Fare,Fast Food, Filipino, French, German, Greek, Health Food, Hungarian,Indian, Indonesian, Irish, Italian, Jamaican, Japanese, Korean, Kosher,Latin American, Lebanese, Malaysian, Mediterranean, Mexican, MiddleEastern, Moroccan, Noodle Shop, Other, Pan-Asian & Pacific Rim, Polish &Czech, Polynesian, Portuguese, Puerto Rican, Russian, Scandinavian,Seafood, South American, Southern & Soul, Southwestern, Spanish,Steakhouse, Tapas/Small Plates, Thai, Tibetan, Turkish, Vegan,Vegetarian, Venezuelan, Vietnamese], a user's cost bracket preferences(e.g., under $20, $21-30, $31-40, above $40), dining locationpreferences (e.g., city, state, and zip code), and the like. Further,information relating to the distance a user is willing to travel (e.g.,1 mile, 5 miles, 10 miles, 20 miles, 50 miles), a user's willingness totry new foods [e.g., play it safe/comfort food, explorer (as long as Ican recognize it), risk-taker (escargot doesn't sound scary at all), auser's eating habits (e.g., I mostly cook at home, I eat out once totwice a week, I eat three to four times a week, I eat out almost everynight).

In the example of FIG. 2B, mutual profile data 202D is stored in dataset202. Mutual profile data includes, but is not limited to, informationrelated to a user's profile, a target member's profile, as well as amutual profile's status, blender value, and availability. In the exampleof FIG. 2C, restaurant profile data 204A and restaurant wish list data204B is stored in dataset 204. Restaurant profile data includesinformation related to a restaurant. Restaurant profile data includes,but is not limited to, restaurant name, price range, cuisine, location(e.g., city, state, zip), rating, recommended and suggested restaurants,and promotions. Restaurant wish list data includes information relatedto restaurant(s) in which a user is interested in dining. Wish list dataincludes, but is not limited to, restaurant name, cuisine, location,rating, ranking and the like.

In the example of FIG. 2D, event data 206 is stored in dataset 206.Event data includes information related to events created for users ofthe social dining system. Event data includes, but is not limited to,information associated with a user's availability, advanced scheduler,location, upcoming and past events.

In the example of FIG. 2E, friend manager data 208A and member wish listdata 208B are stored in dataset 208. Friend manager data includesinformation related to the management of a user's friends and otherindividuals that potentially may be friends. Friend manager dataincludes, but is not limited to, information associated with a user'sfriends, friends' profiles, friend status, invited friends,recently-added friends, restaurants recently-rated by friends, historyof friend activity and the like. Member wish list data includesinformation related to the members of the social dining system whom arenot friends of the user. Member wish list data includes, but is notlimited to, user's wish list of target members and target member dataorganized by attribute.

FIG. 3 depicts an example 300 flow diagram illustrating an exampleprocess for arranging an event by matching a blended profile with otherentities (e.g., individual members, blended profiles) using a blendingalgorithm, according to one embodiment.

At block 302, the system receives parameters which give rise to at leastone blended profile. Through the various modules described in FIG. 2A,the system maintains at least one blended profile with which to pairother entities such as individual(s) and/or other blended profile(s)[i.e., individuals that are part of the blended profile(s)]. As anexample, the system receives information through which a blended profileis created, comprising a user and the users' spouse. Both the user andthe users' spouse are members of the social dining system and each have,for the most part, indicated their preferences in his or her individualprofiles (e.g., lifestyle, foodie, professional). In other words, thesystem is not only aware of the user's personal, professional, andfoodie profile, but also the personal, professional, and foodie profileof a target member with whom the user would like to interact. Similarly,the system is aware of the spouse's personal, professional, and foodieprofile, and those of a target member with whom the spouse would like tointeract. Moreover, the system also knows of the friends, restaurantwish list, member wish list, the availability, restaurant ratings, andmore of both the user and the user's spouse. With all this informationwithin the blended profile, the system contains a plethora of datarelating to the user and spouse, and can perform various comparisonsbetween the data.

At block 304, the system receives parameters which give rise to creatingan event. Through the various modules described in FIG. 2A, the systemcan coordinate the information necessary to create an event by matchinga blended profile with other entities using a blending algorithm. Fromthe above example, the system not only maintains the data relating toeach user of a blended profile, but also the data relating to othermembers who may be matched to the blended profile, To illustrate, MemberA and Member B are also users of the social dining system and each deemsthe other a “friend” in the system as they are work colleagues. As such,Member A and B have a blended profile together. Member X and Member Yare also users of the social dining system, but each member does notknow any other user. The system is privy to varying degrees of profileinformation for the user, the user's spouse, Member A, Member B, MemberX, and Member Y. Nonetheless, each individual is associated withsufficient attributes (as previously described above), such aspreferences, interests, and information such that an event can bearranged. For example, each person has indicated an interest inattending a group meal, his/her availability on the calendar, as well asa preference for a geographic location. As such, the system hassufficient information to potentially create a social dining event amongthe six individuals. However, whether these individuals are matched bythe blending algorithm depends, at least in part, on how muchcommonality there is between certain attributes.

At block 306, the system identifies at least one blended profile, theattributes for each member of the blended profile, including the mutualattributes (i.e., commonality between each member in the blendedprofile). Through the various modules described in FIG. 2A, the systemselects a blended profile with which to create an event and apply theblending algorithm. From the above example, the system may select tocreate an event using the user/spouse blended profile and/or the MemberX/Y blended profile. Further, the system ascertains the individualattributes associated with each member of the blended profile, and themutual attributes, that is the attributes that are same or substantiallythe same value for each of the members of the blended profile. As anexample of a mutual attribute, the user/spouse blended profile likelyhas mutual attributes in terms of geographic preferences and restaurantratings, and the Member A/B blended profile likely has similar mutualattributes in terms of industry as the members are work colleagues.Other mutual attributes can include willingness to try new foods,willingness to travel a certain distance, similar target memberattributes, etc.

At block 308, the system applies weights to the mutual attributes. Aspreviously described, a weighting can be applied to each attribute basedon the importance of the attribute. The blending algorithm can factorinto consideration these weights as an intelligent mechanism throughwhich a blended profile is matched. Take as an example the instance thatblended profile A and B have identical attributes on all accounts exceptthat a mutual attribute of blended profile A is a “willingness to travelonly ten miles to attend an event” and a mutual attribute of blendedprofile B is a “willingness to travel up to 100 miles.” Rather thandesignating the match as a near perfect match, weighting the“willingness to travel” mutual attribute as more important would allowthe system to more intelligently assess the match. This step may bedesigned such that the same weight is applied to each attribute and noattribute is weighed more heavily.

At block 310, the system uses the blending algorithm to match theblended profile with at least one entity [e.g., individual user(s),other blended profile(s)]. Implementing the techniques previouslydescribed, the system can apply the blending algorithm to pair theblended profile with one or more individuals. In one embodiment, theblending algorithm determines how to create a social dining event (orgroup meal) for six people. For example, the system may apply theblending algorithm such that three, two-person blended profiles arematched. In another case, the system may apply the blending algorithmsuch that two blended profiles are matched with two individual membersor that the blended profile is matched with four individual members. Inturn, a blended profile may include two or more individuals, and a groupmeal event may be created for three or more individuals. As such, thoseskilled in the art will appreciate the various permutations ofindividual user(s) and blended profile(s) in formulating a social diningevent.

At block 312, the system creates an event with members of the blendedprofile and entities selected by the blending algorithm. Through thevarious modules described in FIG. 2A, the system performs the necessaryfunctions to arrange, schedule, and/or notify the members that werematched by the blending algorithm of the possibility of an event. In oneembodiment, an event request and/or confirmation is sent to each memberand the member has a choice to opt-in or to decline the invitation. Insome cases, all of the matched members are able to learn about the otherpeople who were matched. In other cases, only select or no informationis revealed to the matched user, the matched blended profile entity,and/or all matched members. Fortunately, the user will know at least oneother person at the group event who is part of the user's blendedprofile.

FIGS. 4A-4E depict example screenshots 500A-500E displayed on anelectronic device, according to one embodiment. It will be appreciatedby a person skilled hi the art that the screenshot may incorporatedifferent content and functionalities in a format different from thatshown.

In FIG. 4A, the screenshot depicts an example user interface throughwhich a user can manage his or her blended profiles, according to oneembodiment. As shown in FIG. 4A, a navigation bar utility indicates thatthe user is interfacing with the blended profile modal of the profilessection 408A. Here, the user is able to manage a current, pending, ornew blended profile. For example, by selecting the “Create New BlendedProfile” option 402A, the user is able to initiate the process ofsetting-up a blended profile with another individual. By selecting the“Pending Request” option 404A, the user is able accept or decline apotential blended profile arrangement. By selecting an option withouttext 406A (e.g., with photos of the current members of the blendedprofile), the user is able to view the details of the profile and evenremove it. Those skilled in the art will appreciate the addition ofother functions when managing a user's blended profiles,

In FIG. 4B, the screenshot depicts an example user interface throughwhich a user can create a blended profile, according to one embodiment.After a user has selected the option to “Create New Blended Profile”402A, a user is presented with a brief summary of his or her attributeson the left side 402B and an example set of instructions 404B throughwhich a user can add a friend to the blended profile. In thisembodiment, the user currently has one friend name John Doe with whomthe user can create a blended profile. Those skilled in the art willappreciate that additional friends may be shown and that the individualbeing added to the blended profile does not have to currently maintain a“friend status.” For example, a user may enter an email address of anindividual who is currently not a member of the social dining system andsolicit the creation of a blended profile with this individual. Next,the user indicates how he or she is associated with the chosen friend byselecting a relationship (e.g., business associate, friend, significantother, or spouse). A connection request is sent to the chosen friend byemail, text message, alert, or the like. Here, the connection requestmust be accepted by the chosen friend, upon which the user can managethe blended profile on the main blended profile page 400A.

In FIG. 4C, the screenshot depicts an example user interface throughwhich the attributes of a blended profile can be displayed, according toone embodiment. After a connection request is accepted by the friend, auser can view the details of a blended profile. As shown in FIG. 4C, theuser, Jane Doe, 402B is presented with the details of her blendedprofile with another member, John Doe in a side-by-side summary. On theleft-hand side, a photo of Jane Doe and selection of her basicattributes, such as date of birth, gender, current location, purpose, ishighlighted in a one-page summary 402C. On the right-hand side, similarattributes for John Doe are presented in a one-page summary 404C forease of comparison. John Doe's photo and basic attributes are shown inthe top-half 406C and an excerpt of some of John's other attributes areshown in the bottom-half as a “personal snapshot” 408C, such as favoritefood attribute, willingness to try new foods attribute, and preferreddining locations attribute.

In FIG. 4D, the screenshot depicts an example user interface throughwhich a user can manage his or her events, according to one embodiment.As shown in FIG, 4D, a “Group Meals” section 402D allows a user tointerface with mechanism 410D through which the user submits his or heravailability for events. The user indicates a date she or he isavailable as well as a period of time (e.g., from 6:00 PM to 11:00 PM).Further, the user selects from an existing list of locations or enters anew location, specifying the city, country, state, and zip code. Inturn, the user may utilize the social dining system in an instance whenhe or she is planning to travel to a new location in the future. Oncethe user submits his or her availability, the Events/AvailabilityCalendar 408D displays the user's availability in a calendar format withcertain color codes which indicate the status of a user and/or an eventon a certain date. As shown in FIG. 4D, a different color indicates auser is “available,” an event is “pending,” “scheduled,” or “confirmed,”and that a “past” event had occurred.

In FIG. 4E, the screenshot depicts an example user interface throughwhich a user can view other members of the social dining system to addto his/her user wish list, according to one embodiment. As shown in FIG.4E, a “Browse By Interests” section 402E allows a user to browse adirectory of members that share the same interest as the user and addthem to a user wish list to increase the chances the user is matchedwith them for a group meal. A number of member photos is presented undera specific interest 404E and can be selected by the user. When a userhas interacted (e.g., right-click, left-click, mouse-over) with a memberphoto, a brief description of the member 406E is shown, such as numberof times wish-listed by other members and an option to add the member tothe user's wish list. In the example of FIG. 4E, the member'sinformation only includes a first name, and location for purposes ofprivacy, however, in some embodiments, additional information may berevealed. Once the user has chosen to add the member to his user wishlist, an indicator 412E is shown that confirms the user's action.Further, the member is added to the collection of people in the userwish list 408E, shown in FIG. 4E as “Blender Wish List.” Those skilledin the art will appreciate that the directory of members may beorganized by other attributes such as lifestyle attributes, preferreddining locations, willingness to try new foods, favorite foods, purpose,restaurant wish list, and other attributes beyond those described.

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known or conventional details are not described in orderto avoid obscuring the description.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Reference inthis specification to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment of thedisclosure. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Moreover, various features are describedwhich may be exhibited by some embodiments and not by others. Similarly,various requirements are described which may be requirements o someembodiments but not other embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. The use of examplesanywhere in this specification including examples of any terms discussedherein is illustrative only, and is not intended to further limit thescope and meaning of the disclosure or of any exemplified term.Likewise, the disclosure is not limited to various embodiments given inthis specification.

The terminology used in the description presented below is intended tobe interpreted in its broadest reasonable manner, even though it isbeing used in conjunction with a detailed description of certainspecific examples of the invention. Certain terms may even be emphasizedbelow; however, any terminology intended to be interpreted in anyrestricted manner will be overtly and specifically defined as such inthis Detailed Description section.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this patent application, shallrefer to this application as a whole and not to any particular portionsof this application. Where the context permits, words in the aboveDetailed Description using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or,” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any of the items in the list, all of theitems in the list, and any combination of the items in the list.

The above detailed description of embodiments of the disclosure is notintended to be exhaustive or to limit the teachings to the precise formdisclosed above. While specific embodiments of, and examples for, thedisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of thedisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are presented in a given order,alternative embodiments may perform routines having steps, or employsystems having blocks, in a different order, and some processes orblocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or sub-combinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallel,or may be performed at different times. Further any specific numbersnoted herein are only examples: alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

While the above description describes certain embodiments of thedisclosure, and describes the best mode contemplated, no matter howdetailed the above appears in text, the teachings can be practiced inmany ways. Details of the system may vary considerably in itsimplementation details, while still being encompassed by the subjectmatter disclosed herein. As noted above, particular terminology usedwhen describing certain features or aspects of the disclosure should notbe taken to imply that the terminology is being redefined herein to berestricted to any specific characteristics, features, or aspects of thedisclosure with which that terminology is associated. In general, theterms used in the following claims should not be construed to limit thedisclosure to the specific embodiments disclosed in the specification,unless the above Detailed Description section explicitly defines suchterms. Accordingly, the actual scope of the disclosure encompasses notonly the disclosed embodiments, but also all equivalent ways ofpracticing or implementing the disclosure under the claims.

1. A method of scheduling a social dining event by matching a blendedprofile with one or more entities: receiving profile parameters whichenable a blended profile to be created, wherein the blended profile isan abstract data structure including information relating to at leasttwo or more members, wherein the at least two or more members areacquainted with each other, and wherein the profile parameters includeattributes of each member of the at least two or more members; receivingevent parameters which enable the social dining event to be scheduled,wherein the social dining event arranges an appointment between the atleast two or more members and the one or more entities at a restaurant,and wherein the event parameters includes availability attributes ofeach member of the at least two or more members; identifying a firstblended profile, including information relating to the at least two ormore members of the first blended profile, wherein the informationincludes, at least, a first set of attributes for a first member and asecond set of attributes for a second member, and wherein the firstmember and the second member are acquainted with each other; determininga set of mutual attributes, wherein the set of mutual attributes includethe common attributes between the first set of attributes and the secondset of attributes; applying a weight to each mutual attribute in the setof mutual attributes; using a blending algorithm to match the blendedprofile with one or more entities based, at least in part, on the set ofmutual attributes, wherein the one or more entities is either a membernot including the first or second member, or a blended profile notincluding the first blended profile; and arranging a first social diningevent between the first member and the second member and the one or moreentities matched by the blending algorithm.
 2. The method of claim 1,wherein the set of mutual attributes includes one of the following:preferred geographical locations, willingness to travel, favoritecuisines, activities, interests.
 3. The method of claim 1, whereinidentifying a first blended profile further includes identifying a firstuser wish list for the first member and identifying a second user wishlist for the second member; and wherein the set of mutual attributesfurther includes the common users between the first user wish list andthe second user wish list.
 4. The method of claim 1, wherein theblending algorithm utilizes a quality-thresholding clustering.
 5. Themethod of claim 1, wherein identifying a first blended profile includesidentifying a first restaurant wish list for the first member andidentifying a second restaurant wish list for the second member; andwherein the set of mutual attributes further includes the commonrestaurants between the first restaurant wish list and the secondrestaurant wish fist.
 6. The method of claim 1, wherein using theblending algorithm further includes: matching the first blended profilewith a second blended profile and matching the first blended profilewith a third blended profile.
 7. A system for creating a social event bymatching a mutual profile with one or more entities, comprising: aprocessor; a mutual profile module instantiated on the system whichoperates to: receive profile parameters which enable a mutual profile tobe created, wherein the mutual profile is an abstract data structureincluding information relating to at least two or more members, whereinthe at least two or more members are acquainted with each other, andwherein the profile parameters include attributes of each member of theat least two or more members; and an event scheduling module, coupled tothe mutual profile module, instantiated on the system which operates to:receive event parameters which enable a social event to be scheduled,wherein the social event arranges a meeting between the at least two ormore members and the one or more entities at a restaurant, and whereinthe event parameters includes availability attributes of each member ofthe at least two or more members and each entity of the one or moreentities; identify a first mutual profile, including informationrelating to the at least two or more members of the first mutualprofile, wherein the information includes, at least, a first set ofattributes for a first member and a second set of attributes for asecond member, and wherein the first member and the second member areacquainted with each other; determine a set of mutual attributes,wherein the set of mutual attributes include common attributes betweenthe first set of attributes and the second set of attributes; apply aweight to each mutual attribute in the set of mutual attributes; use ablending algorithm to match the mutual profile with one or more entitiesbased, at least in part, on the set of mutual attributes, wherein theone or more entities is either a member not including the first orsecond member, or a mutual profile not including the first mutualprofile; and arrange a first social event between the first member andthe second member and the one or more entities matched by the blendingalgorithm.
 8. The system of claim 7, wherein the set of mutualattributes includes one of the following: preferred geographicallocations, willingness to travel, favorite cuisines, activities,interests.
 9. The system of claim 7, wherein the system furthercomprises a friends module, coupled to the mutual profile module,instantiated on the system which operates to identify a first user wishlist for the first member and to identify a second user wish list forthe second member; and wherein the set of mutual attributes furtherincludes the common users between the first user wish list and thesecond user wish list.
 10. The system of claim 7, wherein the systemfurther comprises a restaurants module, coupled to the mutual profilemodule, instantiated on the system which operates to identify a firstrestaurant wish list for the first member and to identify a secondrestaurant wish list for the second member; and wherein the set ofmutual attributes further includes the common restaurants between thefirst restaurant wish list and the second restaurant wish list.
 11. Thesystem of claim 7, wherein using a blending algorithm further includes;matching the first mutual profile with a second mutual profile andmatching the first mutual profile with a third mutual profile.
 12. Thesystem of claim 7, wherein the blending algorithm utilizes aquality-thresholding clustering.
 13. A social dining method of arrangingan event by matching a blended profile with at least two other blendedprofiles: receiving profile parameters which enable a blended profile tobe created, wherein the blended profile is a single merged entityincluding information on two members, wherein the two members areacquainted with each other, and wherein the profile parameters includeattributes of each member of the two members; receiving event parameterswhich enable a social event to be scheduled, wherein the social eventarranges a gathering between the two members and at least two otherblended profiles at a restaurant, and wherein the event parametersincludes availability attributes of each member of the two member andeach member of the at least two other blended profiles; identifying afirst blended profile with a first member and a second member, whereinidentifying includes determining a first set of attributes for the firstmember and a second set of attributes for the second member, and whereinthe first member and the second member are acquainted with each other;determining a set of mutual attributes, wherein the set of mutualattributes include the common attributes between the first set ofattributes and the second set of attributes; using a blending algorithmto match the blended profile with at least two other blended profilesbased, at least in part, on the set of mutual attributes, wherein the atleast two other blended profiles do not include the first member and donot include the second member; and arranging a first social eventbetween the first member and the second member and the at least twoother blended profiles matched by the blending algorithm.
 14. The socialdining method of claim 13, wherein the set of mutual attributes includesone of the following: preferred geographical locations, willingness totravel, favorite cuisines, activities, interests.
 15. The social diningmethod of claim 13, wherein identifying a first blended profile furtherincludes identifying a first user wish list for the first member andidentifying a second user wish list for the second member; and whereinthe set of mutual attributes further includes the common users betweenthe first user wish list and the second user wish list.
 16. The socialdining method of claim 13, further comprises applying a weight to eachmutual attribute in the set of mutual attributes.
 17. The social diningmethod of claim 13, wherein the blending algorithm utilizes aquality-thresholding clustering.
 18. The social dining method of claim13, wherein identifying a first blended profile includes identifying afirst restaurant wish list for the first member and identifying a secondrestaurant wish list for the second member; and wherein the set ofmutual attributes further includes the common restaurants between thefirst restaurant wish list and the second restaurant wish list.
 19. Thesocial dining method of claim 13, wherein using the blending algorithmfurther includes: matching the first blended profile with a secondblended profile and matching the first blended profile with a thirdblended profile.